Learning English and employability

Tas Viglatzis
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Reading time: 4 minutes

English not only opens up career opportunities beyond national borders; it is a key requirement for many jobs. It’s also no longer a case of just learning English for employability, but mastering English for business – and that means an on-going commitment to learn.

My experience is consistent with this trend. If I had to estimate the value that being fluent in English has had on my career, I'd say it was my entire life’s earnings. Learning English has offered me educational options beyond the borders of my own country and enabled me to develop the skills to work for global companies that operate across national boundaries. I have been privileged to work in different countries in roles that have spanned functions, geographies and markets – and my ability to learn and evolve my English skills has been an underlying factor throughout.

Mastering English for employability
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How I improved my English

I grew up in Greece, where – as is common in many European countries with languages that aren’t widely spoken – learning a second European language is part of the education system. As a result, my first encounter with English was through the Franklin approach, which focuses on grammar and writing rather than all the four core skills. This meant I could read and write very well, yet when I first arrived in the UK I couldn’t hold a simple conversation in the pub.

It’s a common problem with traditional educational approaches that don’t cover all the learning skills – something still evident in many countries globally. In Japan, for example, after years of focusing on reading and writing there’s now a strong desire to teach students to understand spoken English and speak fluently.

I was fortunate to complete my postgraduate studies in the UK. This was challenging because I had to raise my English to a much higher standard. I also had to improve my conversational English to enjoy a social life, which was a pretty powerful motivator. I surrounded myself with people who only spoke English, because hearing and using the language regularly are powerful tools in more easily understanding others, and making yourself understood.

Precision learning in the workplace

This approach, together with formal learning, eventually paid off for me. However, once I entered the workplace, I found that there were many new aspects of English to learn.

One of the main barriers to learning at different levels and applications of English – for university, for friendships, for work – is knowing precisely where you are at any stage of your learning journey, and knowing where you need to go next (and how best to get there). As I found, this is compounded when you enter the workplace where you also have new skills to learn in a second language, from IT to soft skills such as negotiation.

If I had been given a precise learning path to help me in my first job, or to get my second job and so on, then my end goals could have been reached more quickly and easily. Yes, I was highly motivated to learn, but what worked for me does not necessarily work for others – especially since professionals who learn English for work often have very little time.And with greater technology advances, we all now expect much faster results.

Crucially, whatever the learning methods and tools are, this points to two most challenging factors in English teaching and learning: making real progress and staying motivated.

In the past, progress in language learning has been measured in broad levels. Common scales, and the curriculum tied to them, are not always best designed to reflect the four skills or different applications, such as academic versus business.

These measures were increasingly being exposed as incomplete and no longer being fit for purpose. It is that need that led to the extensive global research and development into the Global Scale of English (GSE) – a precise, standardised measure of proficiency from 10 to 90 across the four skills.

The GSE extends the Common European Framework of Reference (CEFR); its steps are much more granular and it provides a powerful, focused motivator for further learning throughout your career. It includes sets of learning objectives as "can do" statements at each level; because these are tailored to the learning environment – for example, for work – learning goals and measures of progress are more relevant and accurate. That, in turn, leads to greater engagement and increased motivation.

The future of language learning in the workplace

The key trends of personalisation and adaptive learning are driving the future of English language learning. Online methods and the use of big data analytics and tools continues to expand and increase in sophistication, enabling English language learning to become more specific to individual needs, learning styles and capabilities while offering improved measurement of impact and results. This is vital because language learners learn at different rates and in different ways.

Technology is also helping us support another trend: increasing demand by learners for specific interventions and focus on micro-skills and competencies. As our ability to understand individual needs improves we will become better equipped to provide solutions that concentrate on what’s important for English learners and their careers, such as interviewing in English, running an effective meeting or being able to better express thoughts in writing.

There is a big opportunity for English language learning to embrace those trends, which is why we’re so excited with what we’re doing with the GSE: it gives us the necessary framework to offer flexibility of learning and increased levels of personalisation to English language learners, while ensuring that our products and solutions complement each other. Crucially, it also allows us to measure and show our learners their actual progress.

Finally, it’s important to remember that we never stop learning. Even after living in the UK for 20 years, I still find new words and expressions that open up new possibilities – both in and out of the workplace.

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    In teen classes

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    • What are you worried about?
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    • In your right hand, write what you do when you feel frustrated.
    • In your right foot, write your favorite song.
    • In your left leg, write the name of the person you get good advice from.
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    • On your stomach write your favorite meal.

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    Next, we validate the trained engine by feeding in many more human-marked items, and check that the machine scores are very highly correlated to the human scores. If this doesn’t happen for any item, we remove it, as it must match the standard set by human markers. We expect a correlation of between .95-.99. That means that tests will be marked between 95-99% exactly the same as human-marked samples. 

    This is incredibly high compared to the reliability of human-marked speaking tests. In essence, we use a group of highly expert human raters to train the AI engine, and then their standard is replicated time after time.  

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    Similarly to our speech recognition acoustic models, we first establish a language-specific text recognition model. We feed a large amount of text into the system, and LSA uses artificial intelligence to learn the patterns of how words relate to each other and are used in, for example, the English language. 

    Once the language model has been established, we train the engine to score every written item on a test. As in speaking items, we do this by using human expert raters to score the items first, using double marking. They score many hundreds of written responses for each item, and these ‘Standards’ are then used to train the engine. We then validate the trained engine by feeding in many more human-marked items, and check that the machine scores are very highly correlated to the human scores. 

    The benchmark is always the expert human scores. If our AI system doesn’t closely match the scores given by human markers, we remove the item, as it is essential to match the standard set by human markers.

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    There is nothing wrong with hand-marking homework tests and exams. In fact, it is essential for teachers to get to know their students and provide personal feedback and advice. However, manually correcting hundreds of tests, daily or weekly, can be repetitive, time-consuming, not always reliable and takes time away from working alongside students in the classroom. The use of AI in formative and summative assessments can increase assessed practice time for students and reduce the marking load for teachers.

    Language learning takes time, lots of time to progress to high levels of proficiency. The blended use of AI can:

    • address the increasing importance of formative assessmentto drive personalized learning and diagnostic assessment feedback 

    • allow students to practice and get instant feedback inside and outside of allocated teaching time

    • address the issue of teacher workload

    • create a virtuous combination between humans and machines, taking advantage of what humans do best and what machines do best. 

    • provide fair, fast and unbiased summative assessment scores in high-stakes testing.

    We hope this article has answered a few burning questions about how AI is used to assess speaking and writing in our language tests. An interesting quote from Fei-Fei Li, Chief scientist at Google and Stanford Professor describes AI like this:

    “I often tell my students not to be misled by the name ‘artificial intelligence’ — there is nothing artificial about it; A.I. is made by humans, intended to behave [like] humans and, ultimately, to impact human lives and human society.”

    AI in formative and summative assessments will never replace the role of teachers. AI will support teachers, provide endless opportunities for students to improve, and provide a solution to slow, unreliable and often unfair high-stakes assessments.

    Examples of AI assessments in ELT

    At app, we have developed a range of assessments using AI technology.

    Versant

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    PTE Academic

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